README.md

---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-NLI-idk-mrc-nli-drop-with-xlm-roberta-large
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# fine-tuned-NLI-idk-mrc-nli-drop-with-xlm-roberta-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0610
- Accuracy: 0.9777
- F1: 0.9777

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0581        | 0.5   | 39   | 0.6855          | 0.5079   | 0.3506 |
| 0.7217        | 1.0   | 78   | 0.2164          | 0.9293   | 0.9292 |
| 0.4239        | 1.5   | 117  | 0.1141          | 0.9686   | 0.9686 |
| 0.1448        | 2.0   | 156  | 0.0929          | 0.9660   | 0.9660 |
| 0.1448        | 2.5   | 195  | 0.0677          | 0.9777   | 0.9777 |
| 0.103         | 3.0   | 234  | 0.0933          | 0.9751   | 0.9751 |
| 0.0826        | 3.5   | 273  | 0.0723          | 0.9764   | 0.9764 |
| 0.0598        | 4.0   | 312  | 0.0610          | 0.9777   | 0.9777 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2